R2D2

Function used to set up R2D2 priors for population-level effects in
brms. The function does not evaluate its arguments -- it exists purely
to help set up the model.

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include both theory-driven and
data-driven non-linear terms, auto-correlation structures, censoring and
truncation, meta-analytic standard errors, and quite a few more.
In addition, all parameters of the response distribution can be predicted
in order to perform distributional regression. Prior specifications are
flexible and explicitly encourage users to apply prior distributions that
actually reflect their prior knowledge. Models can easily be evaluated and
compared using several methods assessing posterior or prior predictions.
References: Bürkner (2017) <doi:10.18637/jss.v080.i01>;
Bürkner (2018) <doi:10.32614/RJ-2018-017>;
Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian Bürkner

Jonah Gabry

Sebastian Weber

Andrew Johnson

Martin Modrak

Hamada S. Badr

Frank Weber

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

R2D2 function

<dl><dt>mean_R2</dt>
<dd>mean of the Beta prior on the coefficient of determination R^2.</dd>
<dt>prec_R2</dt>
<dd>precision of the Beta prior on the coefficient of determination R^2.</dd>
<dt>cons_D2</dt>
<dd>concentration vector of the Dirichlet prior on the variance
decomposition parameters.</dd>
<dt>autoscale</dt>
<dd>Logical; indicating whether the R2D2
prior should be scaled using the residual standard deviation
<code>sigma</code> if possible and sensible (defaults to <code>TRUE</code>).
Autoscaling is not applied for distributional parameters or
when the model does not contain the parameter <code>sigma</code>.</dd></dl>

Arguments

R2-D2 Priors in brms — R2D2

<dl>

<dt>mean_R2</dt>
<dd>mean of the Beta prior on the coefficient of determination R^2.</dd>


<dt>prec_R2</dt>
<dd>precision of the Beta prior on the coefficient of determination R^2.</dd>


<dt>cons_D2</dt>
<dd>concentration vector of the Dirichlet prior on the variance
decomposition parameters.</dd>


<dt>autoscale</dt>
<dd>Logical; indicating whether the R2D2
prior should be scaled using the residual standard deviation
<code>sigma</code> if possible and sensible (defaults to <code>TRUE</code>).
Autoscaling is not applied for distributional parameters or
when the model does not contain the parameter <code>sigma</code>.</dd>

</dl>

R2-D2 Priors in brms

R2D2: R2-D2 Priors in brms

Description

Usage

Arguments

References

See Also

Examples